The authors of Fuzzing explore a number of open source fuzzing frameworks available today, including SPIKE, Autodafé, and GPF. They also provide a real-world fuzzing problem and the discuss the development of a solution.

There are a number of available specialized fuzzing utilities which target many common and documented network protocols and file formats. These fuzzers exhaustively iterate through a designated protocol and can be used across the board to stress test a variety of applications that support that protocol. For instance, the same specialized SMTP fuzzer could be used against a variety of e-mail transfer programs such as Microsoft Exchange, Sendmail, qmail, etc. Other "dumb" fuzzers take a more generic approach to allow for fuzzing of arbitrary protocols and file formats and perform simple, non-protocol-aware mutations such as bit flipping and byte transposing.

Although these fuzzers are effective against a wide range of common applications, we often have a need for more customization and thorough fuzzing for proprietary and previously untested protocols. This is where fuzzing frameworks become extremely useful.

In this chapter, we explore a number of open source fuzzing frameworks available today, including SPIKE, the ever popular framework which has become a household name (depending on how geeky your household is). We also look at some exciting newcomers in the field such as Autodafé and GPF. Following the dissection of the existing technologies we then see how, despite the power supplied by many general-purpose fuzzing frameworks, we will still need to create a fuzzer from scratch once in a while. We'll illustrate this point later with a real-world example fuzzing problem and the development of a solution. Finally, we introduce a new framework developed by the authors and explore the advances made by the effort.

What Is a Fuzzing Framework?

Some of the fuzzing frameworks available today are developed in C, while others in Python or Ruby. Some offer functionality in their native language, whereas others leverage a custom language. For instance, the Peach fuzzing framework exposes constructs in Python, while dfuz implements its own set of fuzzing objects (both of these frameworks are discussed in more detail later in the chapter). Some abstract data generation and others don't. Some are object oriented and well documented; others are usable for the most part only by the creator. However, the common goal of all fuzzing frameworks is the same; to provide a quick, flexible, reusable, and homogenous development environment to fuzzer developers.

A good fuzzing framework should abstract and minimize a number of tedious tasks. To assist with the first stages of protocol modeling, some frameworks include utilities for converting captured network traffic into a format understandable by the framework. Doing so allows a researcher to import large chunks of empirical data and focus his efforts on a more human-suitable task such as determining protocol field boundaries.

Automatic length calculation is an absolute necessity for a well-rounded framework. Many protocols are implemented using a TLV (Type, Length, Value) style syntax, similar to the ASN.11 standard. Consider an example where the first byte of data communication defines the type of data to follow: 0x01 for plain text and 0x02 for raw binary. The next two bytes define the length of the data to follow. Finally, the remaining bytes define the value, or the data, specific to the communication as shown here:

When fuzzing the Value field of this protocol, we must calculate and update the two-byte length field in every test case. Otherwise, we risk our test cases getting immediately dropped if the communication is detected as breaching protocol specifications. Calculating Cyclic Redundancy Check (CRC)2 calculations and other checksum algorithms are other tasks a useful framework should include. CRC values are commonly found embedded in both file and protocol specifications to identify potentially corrupted data. PNG image files, for example, employ CRC values that allow programs to avoid processing an image if the received CRC does not match the calculated value. Although this is an important feature for security and functionality, it will prohibit fuzzing efforts if the CRC is not correctly updated as a protocol is mutated. As a more extreme example, consider the Distributed Network Protocol (DNP3)3 specification, which is utilized in Supervisory Control and Data Acquisition (SCADA) communications. Data streams are individually sliced into 250-byte chunks and each chunk is prefixed with a CRC-16 checksum! Finally, consider that the IP addresses of the client, server, or both are frequently seen within transmitted data and that both addresses might change frequently during the course of a fuzz test. It would be convenient for a framework to offer a method of automatically determining and including these values in your generated fuzz test cases.

Most, if not all, frameworks provide methods for generating pseudo-random data. A good framework will take a further step by including a strong list of attack heuristics. An attack heuristic is nothing more than a stored sequence of data that has previously been known to cause a software fault. Format string (%n%n%n%n) and directory traversal (../../../) sequences are common examples of simple attack heuristics. Cycling through a finite list of such test cases prior to falling back on random data generation will save time in many scenarios and is a worthwhile investment.

Fault detection plays an important role in fuzzing and is discussed in detail in Chapter 24, "Intelligent Fault Detection." A fuzzer can detect that its target might have failed at the most simple level if the target is unable to accept a new connection. More advanced fault detection is generally implemented with the assistance of a debugger. An advanced fuzzing framework should allow the fuzzer to directly communicate with a debugger attached to the target application or even bundle a custom debugger technology altogether.

Depending on your personal preference, there may be a long laundry list of minor features that can significantly improve your fuzzer development experience. For example, some frameworks include support for parsing a wide range of formatted data. When copying and pasting raw bytes into a fuzzing script, for example, it would be convenient to be able to paste hex bytes in any of the following formats: 0x41 0x42, \x41 \x42, 4142, and so on.

Fuzzing metrics (see Chapter 23, "Fuzzer Tracking") have also received little attention to date. An advanced fuzzing framework may include an interface for communicating with a metric gathering tool, such as a code coverage monitor.

Finally, the ideal fuzzing framework will provide facilities that maximize code reuse by making developed components readily available for future projects. If implemented correctly, this concept allows a fuzzer to evolve and get "smarter" the more it is used. Keep these concepts in mind as we explore a number of fuzzing frameworks prior to delving into the design and creation of both a task-specific and general-purpose custom framework.